23 changed files with 913 additions and 216 deletions
@ -0,0 +1,149 @@ |
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{ |
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"mac": "mac123", |
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"server": { |
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"port": 2230 |
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}, |
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"fps": { |
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"data": 5, |
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"video": 0 |
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}, |
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"alert": { |
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"enable": false, |
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"intervalSec": 6 |
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}, |
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"win": { |
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"enable": true, |
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"method": "median", |
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"size": 5, |
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"threshold": -0.1, |
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"imgThreshold": 10.0 |
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}, |
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"upload": { |
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"mqtt": { |
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"broker": "218.3.126.49", |
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"port": 1883, |
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"topic": "wybb/zj/mqtt179_debug", |
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"username": "", |
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"password": "", |
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"client_id": "wybb_zj_123_debug" |
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}, |
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"enable": true |
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}, |
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"capture": "rtsp://192.168.1.10:554/user=admin&password=&channel=1&stream=0.sdp?", |
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"targets": { |
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"101": { |
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"info": { |
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"rectangle_area": { |
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"x": 758, |
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"y": 1267, |
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"w": 111, |
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"h": 108 |
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}, |
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"threshold": { |
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"binary": 100, |
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"gauss": 1, |
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"gradient": 100, |
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"anchor": 80 |
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}, |
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"radius": 40.0, |
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"id": 101, |
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"desc": "T_1", |
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"base": false |
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}, |
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"perspective": [ |
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[ |
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1.0, |
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0.0, |
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0.0 |
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], |
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[ |
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0.0, |
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1.0, |
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], |
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[ |
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0.0, |
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1.0 |
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] |
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], |
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"handler_info": null |
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}, |
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"102": { |
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"info": { |
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"rectangle_area": { |
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"x": 2043, |
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"y": 1379, |
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"w": 140, |
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"h": 129 |
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}, |
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"threshold": { |
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"binary": 100, |
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"gauss": 1, |
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"gradient": 100, |
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"anchor": 80 |
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}, |
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"radius": 40.0, |
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"id": 102, |
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"desc": "T_2", |
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"base": false |
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}, |
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"perspective": [ |
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[ |
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1.0, |
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0.0, |
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0.0 |
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], |
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[ |
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0.0, |
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1.0, |
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], |
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[ |
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0.0, |
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0.0, |
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1.0 |
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] |
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], |
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"handler_info": null |
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}, |
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"103": { |
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"info": { |
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"rectangle_area": { |
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"x": 2789, |
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"y": 1410, |
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"w": 152, |
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"h": 142 |
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}, |
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"threshold": { |
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"binary": 100, |
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"gauss": 1, |
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"gradient": 100, |
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"anchor": 80 |
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}, |
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"radius": 40.0, |
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"id": 103, |
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"desc": "T_3", |
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"base": false |
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}, |
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"perspective": [ |
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[ |
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1.0, |
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0.0, |
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0.0 |
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], |
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[ |
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0.0, |
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1.0, |
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0.0 |
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], |
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[ |
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0.0, |
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0.0, |
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1.0 |
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] |
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], |
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"handler_info": null |
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} |
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} |
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} |
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@ -1,25 +1,105 @@ |
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{ |
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"mac": "", |
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"server": { |
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"port": 2230 |
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}, |
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"fps": { |
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"data": 30, |
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"data": 1, |
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"video": 0 |
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}, |
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"alert": { |
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"enable": false, |
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"intervalSec": 60 |
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"enable": true, |
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"intervalSec": 30 |
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}, |
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"upload": { |
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"win": { |
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"enable": true, |
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"method": "median", |
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"size": 20, |
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"threshold": -0.1, |
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"imgThreshold": 10.0 |
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}, |
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"upload": { |
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"mqtt": { |
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"broker": "218.3.126.49", |
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"port": 1883, |
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"topic": "wybb/mqtt", |
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"client_id": "wybb_lk" |
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} |
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"topic": "wybb/zj/mqtt110_debug", |
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"username": "", |
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"password": "", |
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"client_id": "wybb_debug" |
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}, |
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"enable": true |
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}, |
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"capture": "C:/Users/Administrator/Videos/1k.mp4", |
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"targets": { |
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"0": { |
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"info": { |
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"rectangle_area": { |
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"x": 524, |
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"y": 521, |
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"w": 50, |
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"h": 48 |
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}, |
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"threshold": { |
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"binary": 120, |
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"gauss": 1, |
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"gradient": 100, |
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"anchor": 80 |
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}, |
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"radius": 40.0, |
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"id": 0, |
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"desc": "bb_0", |
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"base": false |
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}, |
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"perspective": [], |
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"handler_info": { |
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"is_init": true, |
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"last_detected_time": 1766046547.513531, |
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"radius_pix": 16.498, |
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"pix_length": 2.4245363074312034, |
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"center_point": { |
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"x": 548.511, |
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"y": 543.337 |
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}, |
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"center_init": { |
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"x": 548.515, |
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"y": 543.271 |
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} |
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} |
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}, |
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"1": { |
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"info": { |
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"rectangle_area": { |
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"x": 729, |
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"y": 484, |
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"w": 68, |
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"h": 64 |
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}, |
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"threshold": { |
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"binary": 120, |
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"gauss": 1, |
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"gradient": 100, |
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"anchor": 80 |
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}, |
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"radius": 40.0, |
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"id": 1, |
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"desc": "bb_1", |
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"base": false |
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}, |
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"perspective": [], |
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"handler_info": { |
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"is_init": true, |
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"last_detected_time": 1766046547.513531, |
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"radius_pix": 17.024, |
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"pix_length": 2.3496240601503757, |
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"center_point": { |
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"x": 771.218, |
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"y": 515.245 |
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}, |
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"center_init": { |
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"x": 771.233, |
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"y": 515.17 |
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} |
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} |
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} |
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} |
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} |
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@ -1,23 +1,9 @@ |
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from datetime import datetime |
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import sched |
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import time |
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from time import sleep |
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|
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|
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# 定义任务 |
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def print_event(name): |
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print(f"EVENT: {time.time()} - {name}") |
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def periodic_task(interval): |
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print(f"Task executed at: {datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-2]}") |
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scheduler.enter(interval, 1, periodic_task, (interval,)) |
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|
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def print_hi(name): |
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# 在下面的代码行中使用断点来调试脚本。 |
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print(f'Hi, {name}') # 按 Ctrl+F8 切换断点。 |
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|
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|
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# 按装订区域中的绿色按钮以运行脚本。 |
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if __name__ == '__main__': |
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# 初始化调度器 |
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scheduler = sched.scheduler(time.time) |
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scheduler.enter(0, 1, periodic_task, (0.33,)) # 每 5 秒执行一次 |
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scheduler.run() |
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print("===============") |
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print_hi('PyCharm') |
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|
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|
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@ -1,7 +1,8 @@ |
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numpy~=2.2.2 |
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matplotlib~=3.10.3 |
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dataclasses-json~=0.6.7 |
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flask~=3.1.0 |
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matplotlib~=3.10.3 |
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requests~=2.32.3 |
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opencv-python~=4.10 |
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scipy~=1.16.1 |
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paho-mqtt~=2.1.0 |
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requests~=2.32.3 |
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opencv-python~=4.10.0 |
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@ -0,0 +1,120 @@ |
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# smoothing_window_improved.py |
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import threading |
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from collections import deque |
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from typing import Dict, List, Tuple |
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import numpy as np |
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|
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from models.sampleMsg import AllSensorData, SensorData |
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|
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|
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class AdaptiveSmoothingWindow: |
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""" |
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一个自适应的数据平滑窗口。 |
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当数据波动较小时，使用原始数据；当波动较大时，使用移动平均值进行平滑。 |
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""" |
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|
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def __init__(self,method:str ="median", window_size: int = 11, volatility_threshold: float = 2.0,img_threshold: float = 20.0): |
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""" |
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初始化自适应平滑窗口。 |
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|
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:param window_size: 移动平均窗口的大小，默认为 11。 |
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:param volatility_threshold: 波动阈值。当新数据与上一个平滑值的差异超过此值时， |
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将触发平滑。默认为 2.0。 |
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""" |
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if window_size <= 0: |
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raise ValueError(f"窗口大小 'window_size={window_size}' 必须是正数。") |
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|
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self.method = method |
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self.window_size = window_size |
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self.volatility_threshold = volatility_threshold |
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self.img_Threshold = img_threshold |
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# 内部存储结构: {sensor_pos: deque of (x, y) tuples} |
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self.sensor_history: Dict[str, deque[Tuple[float, float]]] = {} |
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|
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# 存储每个传感器上一次输出的平滑值: {sensor_pos: (last_smoothed_x, last_smoothed_y)} |
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self._last_smoothed_values: Dict[str, Tuple[float, float]] = {} |
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self._lock = threading.Lock() |
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|
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def resize(self,method:str ="median", window_size: int = 11, volatility_threshold: float = 2.0,img_threshold: float = 20.0): |
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with self._lock: |
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self.method = method |
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self.window_size = window_size |
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self.volatility_threshold = volatility_threshold |
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self.img_Threshold = img_threshold |
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# 内部存储结构: {sensor_pos: deque of (x, y) tuples} |
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self.sensor_history: Dict[str, deque[Tuple[float, float]]] = {} |
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|
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# 存储每个传感器上一次输出的平滑值: {sensor_pos: (last_smoothed_x, last_smoothed_y)} |
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self._last_smoothed_values: Dict[str, Tuple[float, float]] = {} |
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|
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def process(self, new_sensor_data: AllSensorData) -> (AllSensorData,bool): |
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""" |
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处理一帧新的传感器数据，根据波动大小自适应地应用平滑。 |
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|
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:param new_sensor_data: 包含所有传感器最新数据的 AllSensorData 对象。 |
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:return: 一个新的 AllSensorData 对象，其中包含了自适应平滑后的数据。 |
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""" |
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smoothed_sensor_list: List[SensorData] = [] |
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is_abnormal=False |
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with self._lock: |
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for sensor in new_sensor_data.data: |
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pos = sensor.pos |
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|
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# 如果是新传感器，初始化其历史队列和上一个平滑值 |
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if pos not in self.sensor_history: |
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self.sensor_history[pos] = deque(maxlen=self.window_size) |
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# 首次出现，将当前值作为“上一个平滑值” |
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self._last_smoothed_values[pos] = (sensor.x, sensor.y) |
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|
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history_queue = self.sensor_history[pos] |
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history_queue.append((sensor.x, sensor.y)) |
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# print(f"====> {pos} 设备 滑窗数据 {len(history_queue)}/{self.window_size}") |
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last_x, last_y = self._last_smoothed_values[pos] |
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|
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# 决定当前使用哪个值作为输出 |
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current_output_x, current_output_y = sensor.x, sensor.y # 默认使用原始值 |
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|
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# 仅当窗口填满后，才考虑进行平滑 |
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if len(history_queue) == self.window_size: |
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# 计算窗口内的值 |
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xs, ys = zip(*history_queue) |
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# 初始化平滑值 |
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if self.method == "median": |
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sma_x = float(np.median(xs)) |
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sma_y = float(np.median(ys)) |
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else: # 默认使用均值 |
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sma_x = float(np.mean(xs)) |
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sma_y = float(np.mean(ys)) |
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|
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# 计算新原始数据与上一个平滑值之间的波动 |
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delta_x = abs(sensor.x - last_x) |
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delta_y = abs(sensor.y - last_y) |
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|
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# 如果 x 或 y 的波动超过阈值，则使用平滑值 |
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if delta_x >= self.volatility_threshold: |
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current_output_x = sma_x |
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if delta_y >= self.volatility_threshold: |
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current_output_y = sma_y |
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|
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#巨大变化 存储图片 |
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if delta_x >= self.img_Threshold: |
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is_abnormal = True |
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if delta_y >= self.img_Threshold: |
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is_abnormal = True |
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|
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# 创建新的 SensorData 对象 |
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smoothed_sensor = SensorData( |
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pos=pos, |
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desc=sensor.desc, |
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x=current_output_x, |
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y=current_output_y |
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) |
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smoothed_sensor_list.append(smoothed_sensor) |
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|
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# 更新“上一个平滑值”为本次的输出值 |
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self._last_smoothed_values[pos] = (current_output_x, current_output_y) |
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|
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return AllSensorData( |
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data=smoothed_sensor_list, |
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time=new_sensor_data.time |
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), is_abnormal |
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@ -0,0 +1,78 @@ |
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# smoothing_window.py |
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|
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from collections import deque |
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from typing import Dict, List, Tuple |
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import numpy as np # 使用 numpy 可以更高效地进行平均值计算 |
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|
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from models.sampleMsg import AllSensorData, SensorData |
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|
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|
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class SmoothingWindow: |
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""" |
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一个用于对传感器数据进行简单移动平均 (SMA) 平滑处理的窗口。 |
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""" |
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def __init__(self, window_size: int = 10): |
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""" |
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初始化平滑窗口。 |
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|
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:param window_size: 移动平均窗口的大小，默认为 10。 |
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""" |
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if window_size <= 0: |
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raise ValueError("窗口大小 'window_size' 必须是正数。") |
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|
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self.window_size = window_size |
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|
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# 内部存储结构: {sensor_pos: deque of (x, y) tuples} |
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# deque 会自动维护固定大小，当新元素加入而队列已满时，最老的元素会被移除 |
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self.sensor_history: Dict[str, deque[Tuple[float, float]]] = {} |
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|
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def process(self, all_sensor_data: AllSensorData) -> AllSensorData: |
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""" |
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处理一帧新的传感器数据，对每个传感器的 x, y 坐标应用移动平均平滑。 |
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|
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:param all_sensor_data: 包含所有传感器最新数据的 AllSensorData 对象。 |
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:return: 一个新的 AllSensorData 对象，其中包含了平滑后的数据。 |
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如果某个传感器的数据点少于窗口大小，则其值不会被平滑（保持原始值）。 |
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""" |
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smoothed_sensor_list: List[SensorData] = [] |
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|
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# 遍历当前帧中的每一个传感器数据 |
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for sensor in all_sensor_data.data: |
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# 如果是新传感器，为其创建一个新的 deque |
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if sensor.pos not in self.sensor_history: |
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self.sensor_history[sensor.pos] = deque(maxlen=self.window_size) |
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|
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# 获取该传感器的历史数据队列 |
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history_queue = self.sensor_history[sensor.pos] |
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|
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# 将新的 (x, y) 数据添加到队列末尾 |
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history_queue.append((sensor.x, sensor.y)) |
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|
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# 检查队列中的数据点数量是否达到了窗口大小 |
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if len(history_queue) == self.window_size: |
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# 如果达到窗口大小，则进行平滑计算 |
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|
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# 使用 numpy 将队列中的 x 和 y 值分别转换为数组，便于计算 |
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# history_queue 是一个 (x,y) 元组的列表，我们用 zip(*...) 来解包 |
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xs, ys = zip(*history_queue) |
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smoothed_x = np.mean(xs) |
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smoothed_y = np.mean(ys) |
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|
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# 创建一个新的 SensorData 对象，使用平滑后的值 |
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# 注意：desc 等其他字段保持不变 |
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smoothed_sensor = SensorData( |
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pos=sensor.pos, |
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desc=sensor.desc, |
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x=smoothed_x, |
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y=smoothed_y |
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) |
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smoothed_sensor_list.append(smoothed_sensor) |
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else: |
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# 如果未达到窗口大小，不进行平滑，直接返回原始数据 |
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smoothed_sensor_list.append(sensor) |
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|
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# 创建并返回一个包含所有平滑后数据的新 AllSensorData 对象 |
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return AllSensorData( |
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data=smoothed_sensor_list, |
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time=all_sensor_data.time # 时间戳保持不变 |
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) |
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@ -0,0 +1,10 @@ |
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from functools import cached_property |
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|
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from stabilize.algorithm.adaptiveSmoothingWindow import AdaptiveSmoothingWindow |
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|
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|
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class Win: |
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@staticmethod |
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def create_window(method,size, threshold,img_threshold): |
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print(f"开启稳定窗口method={method},size={size}, threshold={threshold},img_threshold={img_threshold}") |
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return AdaptiveSmoothingWindow(method,size, threshold,img_threshold) |
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@ -0,0 +1,23 @@ |
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import platform |
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import socket |
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import struct |
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|
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def get_mac_address(interface='eth0') -> str: |
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os_name = platform.system() |
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if os_name=="Windows": |
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return "mac_win_123" |
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else: |
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import fcntl |
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sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) |
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mac = fcntl.ioctl( |
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sock.fileno(), |
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0x8927, |
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struct.pack('256s', interface[:15].encode('utf-8')) |
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) |
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mac_address = ':'.join(['%02x' % b for b in mac[18:24]]) |
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return mac_address |
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|
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|
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if __name__ == '__main__': |
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mac_str=get_mac_address() |
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print(mac_str) |
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